Interconnected Smart Entities for Correlating Diverse IoT Sensor Data
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Solution Overview
Problem
Existing IoT platforms struggle with effective analysis of the exponentially increasing data generated by a large number of interconnected IoT devices, lacking efficient methods for managing and correlating data from diverse sensors.
Innovation Solution
A cloud computing system generates a database of interconnected smart entities, including sensor object entities and data entities, interconnected by relational objects, to manage and update sensor data, supporting various protocols and formats, and calculates averages and abnormalities from past measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large number of IoT devices are interconnected to expand system functionality, then the quantity of data produced increases exponentially, but the ability to effectively analyze and manage this data deteriorates
Solution Approach 1:
The patent segments the data management system into multiple components: edge devices that perform preliminary data processing and filtering, intermediate servers that aggregate data from multiple edge devices, and centralized cloud platforms that handle comprehensive analysis. This hierarchical segmentation allows the system to maintain functionality while improving data analysis effectiveness by distributing processing loads appropriately.
Solution Approach 2:
The patent introduces intermediary components such as protocol translators, data normalization layers, and aggregation servers that mediate between diverse IoT devices and the central analysis platform. These intermediaries standardize data formats, filter redundant information, and prepare data for efficient analysis, thereby resolving the contradiction between system versatility and analysis effectiveness.
2Adaptability or versatility
If diverse sensors with different protocols and formats are integrated into the system, then the adaptability of the system improves, but the complexity of data management increases
Solution Approach 1:
The patent implements a universal data interface layer that can handle multiple sensor protocols and formats through a single standardized API. This multi-functional interface accepts data from various sensor types (temperature, humidity, pressure, motion) using different communication protocols (MQTT, HTTP, CoAP) and automatically normalizes them into a common format, thereby maintaining sensor compatibility while reducing data management complexity.
Solution Approach 2:
The patent creates standardized data templates and schema definitions that represent common sensor data structures. Instead of managing each sensor's unique protocol directly, the system uses copied template structures to represent diverse sensor data in a unified format, simplifying data management while maintaining the ability to accommodate various sensor types through template instantiation.
3Quantity of substance
If all sensor data is collected and stored centrally for comprehensive analysis, then the quantity of available data increases, but the time and resources required for data processing increase
Solution Approach 1:
The patent implements preliminary data processing and filtering at the edge devices before data is transmitted to central servers. Edge devices perform actions such as data validation, anomaly detection, aggregation, and preprocessing, thereby reducing the volume of raw data that needs to be transmitted and processed centrally. This preliminary action maintains comprehensive data availability while significantly reducing central processing time and resource requirements.
Solution Approach 2:
The patent applies partial processing by selectively transmitting only relevant data to central servers based on predefined criteria such as anomaly thresholds, change rates, or business rules. Instead of transmitting all sensor data, the system performs partial actions by filtering and prioritizing data, thereby maintaining adequate data volume for analysis while reducing overall processing time and resource consumption.
Data Source
AI summary
One or more non-transitory computer readable media contain program instructions that, when executed by one or more processors, cause the one or more processors to perform operations including generating a database of interconnected smart entities. The smart entities include sensor object entities representing each of the plurality of sensors and data entities representing measurements received from the sensors. The smart entities are interconnected by relational objects indicating relationships between the smart entities. The instructions cause the one or more processors to receive a new measurement from a first sensor, identify a first data entity from a relational object for the first sensor, the first data entity including a plurality of past measurements received from the first sensor, and modify the first data entity within the database of smart entities to include the new measurement received from the first sensor.